Triple
T18799599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PyTables |
E459726
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | HDF5 library |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: HDF5 library | Statement: [PyTables, basedOn, HDF5 library]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HDF5 library Context triple: [PyTables, basedOn, HDF5 library]
-
A.
h5py
h5py is a Python library that provides a high-level, NumPy-friendly interface for reading and writing HDF5 files used for storing large numerical datasets.
-
B.
HDF
HDF is the acronym for the Hungarian Defence Forces, the unified military organization responsible for Hungary’s national defense and participation in international security operations.
-
C.
HDF
chosen
HDF (Hierarchical Data Format) is a widely used file format and data model designed for storing and organizing large, complex scientific and engineering datasets.
-
D.
h5netcdf
h5netcdf is a Python library that provides a NetCDF4-like interface for reading and writing data stored in HDF5 files.
-
E.
PyTables
PyTables is a Python library that provides efficient management, querying, and storage of large amounts of data using the HDF5 format.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.